As a Software Engineer at AssistRx, you play a pivotal role in building the technology that powers patient access to life-changing therapies. This position is central to the company’s mission of simplifying the patient journey through intelligent software solutions. You will be responsible for developing, maintaining, and scaling applications that bridge the gap between healthcare providers, patients, and pharmaceutical manufacturers.
The work at AssistRx is intellectually demanding and highly impactful. You will be expected to contribute across the stack, ensuring that the software you build is not only performant and reliable but also intuitive for the end-users who rely on these systems daily. Whether you are focused on front-end interfaces or back-end data architecture, your contributions directly influence the efficiency and success of healthcare delivery programs.
Technical Assessment
reportedWhat this round decides is narrow: whether you can produce code that runs and is correct on inputs nobody showed you. An elegant solution that does not compile scores below a plain one that does, so write a correct brute force first, say out loud that you know its cost, and improve it with the working version still on screen. What separates strong answers is who finds the broken case. Trace your own code against an empty input, a single element, and duplicate keys before you say you are finished, because being told is far more expensive than noticing.
What to demonstrate
- Whether degenerate inputs get checked without being asked for: an empty collection, one element, every element equal, and the extreme value the input type allows
- Whether the complexity you state matches the code you actually wrote, including a sort or a copy sitting inside a loop
- Whether the finished answer is verified against the worked examples before you call it done, rather than assumed correct because the code reads correctly
How to prepare
- Take five problems you have already solved and, without running anything, write down what each returns for empty input, a single element, and all-duplicates. Then run them and count how many you predicted wrong.
- Drill the brute force as its own skill: on ten problems, write only the obviously-correct slow version and time how long it takes to get it passing. If that is more than a few minutes, that is what to practise, not the optimal version.
- Add a fixed last step before you submit anything, reading only the loop bounds and the initial value of each accumulator, which is where most off-by-one errors live
Behavioral Interviews
reportedThis round is deciding whether a change you make without supervision can be allowed to reach production. It is scored on what you knew at the moment you decided, not on how it turned out, so a story that opens with the result and works backwards reads as luck retold as judgement. Say what the options were, what you did not know, what you did to shrink the unknown before committing, and what you accepted as the worst plausible case. The detail that separates answers is a bound: how many users, how much data, and for how long, if you had been wrong.
What to demonstrate
- Whether the reasoning you give was available at the time you decided rather than after the result came in, since a story whose deciding evidence arrived later describes an outcome and not a judgement
- Whether you can put units on the exposure (users, rows, minutes of degraded service) and whether the containment you chose actually bounded it: a canary bounds the request path it fronts, while a background job writing to a shared table reaches every user regardless of which version served their requests
- Whether the reversal path existed before you shipped or was improvised during the incident, and whether it restores state or only stops further damage
How to prepare
- For your three largest changes, write down the one thing you would have had to be wrong about for it to fail, and what your best estimate of it was on the day you shipped. If you never held an estimate, that is the gap the follow-up questions will find
- Write the undo procedure for one of those changes as it existed at the time, then mark which steps restore data and which only stop new damage. Turning a flag off or reverting a deploy ends the new writes; rows already written come back only from a copy you kept, and a dropped column comes back empty unless something outside the schema holds the values
- Rehearse one story from the decision point forward and stop before the outcome, then have someone ask what you would do next. If the story only works with the ending attached, it is an anecdote rather than a decision you can defend
PracHub editorial advice for the preparation topics above.
Storing monetary amounts as floating-point numbers.
Binary floating point cannot represent values like 0.10 exactly, so summing allowed and paid amounts across millions of claim lines accumulates representation and rounding error. Reconciliation against a remittance file is exact to the cent, so the drift surfaces as a balance that is off by a few cents with no identifiable cause, and engineers lose days looking for a logic bug in code that is correct apart from its numeric type. Integer minor units or an exact decimal type removes the class entirely.
Writing the access audit record inside the read transaction, or firing it off after the response with no durability.
Inside the transaction, an audit-store outage blocks clinical reads and turns a logging dependency into a care outage. Fire-and-forget afterwards means the audit trail is incomplete during precisely the incidents it exists to reconstruct, and the gaps are invisible until someone asks for the log. The usual resolution is committing the access decision and its audit row together to a local outbox and shipping asynchronously, which keeps the read path available while preserving durability.
Assuming fixed-width integer arithmetic cannot overflow
In languages with fixed-width integers, including C, C++, Java, Go and Rust, computing a midpoint as (lo + hi) / 2 overflows once the sum passes the type's maximum, so write lo + (hi - lo) / 2 instead. Say which language you are in: arbitrary-precision integers, as in Python or Ruby, remove this specific hazard and none of the others.
Assuming the bug is in the framework
Suspect your own code first: read the stack trace top to bottom, check which versions are actually installed rather than which ones you believe are, and reproduce in isolation before blaming a library that thousands of people run daily. When the fault really is upstream, you need that minimal reproduction to say so credibly anyway.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Extract an idempotency key from a raw HL7v2 message
An HL7v2 message arrives as a byte buffer up to 256 KB, segments terminated by carriage return (0x0D), first segment MSH. MSH-1 is the field separator character itself and MSH-2 holds the encoding characters, so splitting the MSH segment on the separator puts MSH-n at index n-1 for n of 2 or more. Return the idempotency key built from MSH-4 sending facility, MSH-3 sending application, MSH-10 message control ID and MSH-7 event timestamp, with escape sequences decoded in each value. Single pass, O(n). Do not hardcode the separator characters.
Approach
- Read the delimiters out of the message instead of assuming them: the byte immediately after MSH is the field separator, and the next field's bytes give component, repetition, escape and subcomponent separators in that order. Everything downstream uses those values, because a partner is entitled to send different ones and the common defaults are a convention, not a guarantee.
- Verify the first three bytes are MSH before anything else and reject otherwise, since a socket read can begin mid-message. Then bound the segment at the first terminator and split that slice only, applying the n-1 offset that the MSH segment alone requires.
- Decode escapes in one left-to-right pass per extracted value: on the escape character, read to the next escape character and map the codes for field, component, subcomponent, repetition and escape back to their literal characters. Treat an unterminated escape as a malformed message rather than dropping the tail silently.
- Normalise MSH-7 to UTC before it enters the key. The timestamp may carry an offset or omit one, and two spellings of the same instant must hash identically or the ledger stops deduplicating the moment a partner changes its formatter.
- Compose the key as the full tuple, not the control ID alone. The control ID is unique only per sending application and some senders roll the counter over, so facility plus application plus control ID plus instant is the smallest key that survives a rollover.
- Cost is O(n) time and O(k) space for the extracted values. Hashing the whole body is also O(n) but cannot distinguish a genuine resend from a corrected retransmission that reuses the control ID, which is a different event.
Worked solution 15 min
- Take the separator characters from bytes 3 through 7 of the buffer and store them.
- Slice to the first segment terminator and split on the field separator into tokens.
- Read tokens at indices 2, 3, 6 and 9 for MSH-3, MSH-4, MSH-7 and MSH-10.
- Unescape each extracted value with the escape character you read in step one, then convert MSH-7 to UTC.
- Return the tuple, and the hash of it if the ledger stores a fixed-width key.
Follow-up
- One partner sends MSH-7 with no timezone offset and another sends it with an offset. What do you store, and what do you compare on?
- A sender rolls its control ID counter over and restarts from zero. Which component of your key absorbs that, and which message pairs would still collide?
- Segments arrive separated by line feed instead of carriage return, or with a trailing empty field. Which should the parser accept and which should it negatively acknowledge?
Resolve enterprise identity from a link log that supports unmerge
person_identity_link holds link_id, enterprise_person_id, assigning_authority, source_person_id, match_score, link_status (auto_linked, manual_linked, potential_duplicate, unlinked, rejected), version, superseded_by_link_id, decided_by, decided_at. You have up to 40 million source identities and 60 million decisions applied in decided_at order. Build a structure answering which enterprise identity a given (assigning_authority, source_person_id) resolves to after any prefix of the log, and supporting a revert of the k most recent merges at O(1) each. State the query complexity, and say why path compression is unavailable to you.
Approach
- Intern the node key as the pair (assigning_authority, source_person_id) into a dense integer index. Never key on source_person_id alone: two facilities in one network routinely issue the same medical record number to different people, so a bare-value key merges two patients before any matching logic has run, and a single-source test fixture will never show it.
- Classify the log rows before touching the structure. auto_linked and manual_linked are unions; potential_duplicate and rejected are recorded decisions that must not union anything; unlinked is a revert of the link it supersedes, not a new edge.
- Use union by size with an explicit undo stack, pushing (attached_root, its previous parent, the previous size of the absorbing root) on every union. Find walks parent pointers to the root: union by size bounds tree height at log2(n), so a query is O(log n) worst case, a union is O(log n), and a revert pops two words and is O(1).
- Path compression is the thing you have to give up, and the reason is specific: it rewrites parent pointers of nodes that were never named in the union being recorded, so the undo entry no longer describes the mutation that happened and a revert restores a forest that is quietly wrong. Near-constant find is only available to an append-only structure.
- Hold enterprise_person_id as a property of the root slot rather than a value copied onto every member. A merge then relabels one slot instead of n rows, and an unmerge restores two labels instead of reconstructing n.
- State the price plainly: every read path gains a resolution hop and no downstream table can carry a plain patient_id column. That cost is what buys reversibility, and it belongs in the design discussion rather than being discovered later by whoever writes the first join.
Follow-up
- A merge is found wrong three weeks and 200,000 unions later, and your stack only reverts a suffix. What do you do instead, and what does that cost?
- A claim was posted under the losing identity before the merge. After the unmerge, which identity owns it, and what in your design let you answer that?
- Two matcher instances submit unions concurrently. What is the smallest change that keeps both the structure and the undo stack consistent?
Sum surviving claim versions in one pass over unordered lines
You are streamed up to 50 million claim_line records in arbitrary order: claim_id, claim_version, line_number, frequency_code (original, replacement, void), enterprise_person_id, allowed_amount_cents. Every version of a claim shares its claim_id, and a replacement arrives as a higher claim_version. Return total allowed_amount_cents per enterprise_person_id, counting each claim once at its highest version and contributing zero when that version is a void. Amounts are non-negative int64 minor units. Target O(N) time and O(C) space for C distinct claims, single pass, no sort.
Approach
- Say the two grains out loud before writing anything: the version lives at claim grain, the money lives at line grain. Every wrong answer here comes from applying a claim-level rule with a line-level filter.
- Keep one hash map from claim_id to a small record (best_version, sum_cents, person_id, is_void). Per line: if claim_version is greater than best_version, reset sum_cents to this line's amount and overwrite the person and void flag; if equal, add to it; if lower, discard the line. That reset is what makes the pass order-independent, so a replacement arriving before its original still wins.
- Resolve the void at the end, not at ingest. A void only nullifies the claim if it is the surviving version, and zeroing on sight would let an earlier replacement that arrives later resurrect the money.
- Fold the C claim records into a person-keyed map in a second phase, O(C). Do not accumulate into the person total during the stream: you cannot subtract a superseded version you have already forgotten.
- Cost is O(N) time, O(C) space. The sort-based alternative, group by (claim_id, claim_version) and keep the max, is O(N log N) and needs the set resident; prefer it only when C approaches N and the map will not fit.
- Keep cents in int64 throughout. Fifty million lines at a realistic per-line ceiling stay four orders of magnitude below 2^63, so the integer type costs nothing and removes the float drift class entirely.
Follow-up
- You shard the stream across eight workers. Sharding by hash of claim_id works; what exactly breaks if you shard by enterprise_person_id instead?
- A replacement arrives for a claim_id whose original never appears in the batch. What does your map produce, and is that the financially correct answer or a reconciliation break?
- The same claim_id is reused by two different submitting organisations. How does the key have to change, and when would you have noticed?
Denormalise consent into a decision table with a stated staleness bound
Access governance answers 'may this requester read this person's data for this purpose' on every identified read — tens of thousands per second at a 5ms p99 — from consent_directive: consent_id, enterprise_person_id, version, scope, purpose_of_use text[], grantee_org_id (NULL meaning all organisations), permit boolean, effective_ts, expires_ts, revoked_ts, status. Evaluating the normalised version history per read misses the budget. Design the denormalised decision store and its key, state the revocation staleness bound in seconds and the mechanism that actually enforces it, and say which normalised rows remain the system of record and why.
Approach
- Derive the shape from the read, not from the source table. The read is a point lookup on (person, grantee organisation, purpose), so fan the purpose_of_use array out into one row per purpose and make those three columns the primary key. A GIN index on the array would serve containment queries nobody issues.
- Handle the wildcard concretely. grantee_org_id NULL means 'all organisations', and NULL never equals anything, so a nullable column in the key breaks both the primary key and the equality lookup. Store a sentinel org id of 0 for the wildcard and resolve specific-before-wildcard in the lookup, or the wildcard row is silently unreachable.
- Make refusal a row, not an absence. An explicit permit = false must outrank a permit for the same key, so carry a precedence rank (specific grantee beats wildcard, refusal beats permit at equal specificity) and resolve with ORDER BY rank LIMIT 1. Default deny when nothing matches, so a rebuild failure fails closed.
- Separate the two staleness mechanisms and be precise about which one is the bound. The in-process decision cache has a TTL; a revocation also publishes an invalidation. The publish shortens the typical case to milliseconds, but it can be lost, so the guaranteed bound is the TTL alone — quote that number, for example 30 seconds, and justify it against the cache hit rate you need to hold 5ms p99.
- Evaluate expires_ts at read time against the request clock rather than baking the decision. A cached permit whose expires_ts has passed is wrong regardless of invalidation, because no event fires when a timestamp simply goes by.
- Keep consent_directive as the system of record: audit must reconstruct the directive in force at any past instant, the decision table holds only current state, and a corrupted decision table must be rebuildable by replaying the directives. Bound the write amplification by keeping grantees at organisation granularity and purposes a closed code set, so one directive expands to a known small number of rows rather than an unbounded cross product.
Worked solution 40 min
- Write the decision table DDL with the three-column primary key, the sentinel wildcard, the precedence rank and a monotonic decision_version.
- Write the lookup query: equality on the key with the wildcard row unioned in, ORDER BY rank, LIMIT 1, and an expires_ts predicate evaluated against the request timestamp.
- Write the projection that turns one consent_directive version into its decision rows, and compute the expansion factor for a directive covering four purposes and one organisation.
- State the TTL, then justify it: at N reads per second and M people, compute the cache hit rate the TTL yields and check it against the 5ms p99 budget.
- Write the failure narrative for a lost invalidation and confirm the TTL, not the bus, is what caps exposure.
- Write the rebuild procedure from consent_directive and the assertion that proves the rebuild is complete before it is swapped in.
Follow-up
- Break-glass must always succeed and must be unmistakably marked. Where does it sit relative to this lookup, and what stops it from becoming the quiet default path?
- The invalidation bus is down for thirty minutes. Walk through what a revoked person's data exposure looks like minute by minute, and what the backlog does when the bus returns.
- Where does the access audit record get written so that an audit-store outage degrades neither the log's completeness nor the read's availability?
Reproduce and fix a lost update on a deductible accumulator
An accumulator row holds deductible_applied_cents bigint and plan_deductible_cents bigint, keyed by (enterprise_person_id, plan_id, benefit_year). The adjudicator opens a transaction, SELECTs the row, computes the member's share in application code, then UPDATEs the row to the absolute new total it computed, all under PostgreSQL's default READ COMMITTED. Two claim lines for one member adjudicate concurrently: both charge the member deductible, but the accumulator advances by only one of the two amounts, so the member is billed deductible again on a later line after the plan deductible has already been met. Write the exact two-session interleaving that produces it. Then give three fixes, each naming the lock or isolation level, the SQLSTATE you must handle, and the throughput cost.
Approach
- State the anomaly precisely. READ COMMITTED takes a fresh snapshot per statement, so it prevents dirty reads but permits a lost update when a transaction reads a value, computes outside the database, and writes back an absolute result. The vulnerable window is the round trip through application code between two statements, not the transaction boundary — the same logic expressed as one relative UPDATE is safe at this very isolation level.
- Write the interleaving as an ordered script both sessions can be replayed from, with the commit points marked, and make both writes absolute (SET deductible_applied_cents = :computed_total). The point is not that two writes happen — it is that the second write stores a total computed from a value that had already been superseded by the time it landed.
- Fix one, SELECT ... FOR UPDATE on the read: the second session blocks on the row lock and, under READ COMMITTED, re-reads the newest committed version when it unblocks, so its computation starts from the winner's total. No serialization error to handle. Cost is serialised throughput per member and a lock held for the transaction's whole duration, so nothing inside may call an external payer.
- Fix two, REPEATABLE READ or SERIALIZABLE with a retry loop: in PostgreSQL, REPEATABLE READ aborts the second writer of a row with SQLSTATE 40001, 'could not serialize access due to concurrent update'; SERIALIZABLE additionally aborts on read/write dependencies detected by SSI, also under 40001. The retry re-runs the whole transaction, so the claim application must be idempotent or keyed by claim_line_id, or a retry double-applies.
- Fix three, single-writer partitioning: route by hash(enterprise_person_id) to one consumer per partition, so no two transactions ever touch one accumulator. No locks, no retries; the cost is head-of-line blocking behind a slow claim, rebalancing during deploys, and losing the ability to scale a hot member.
- Name the cheapest option and its real catch. A single UPDATE ... SET deductible_applied_cents = LEAST(plan_deductible_cents, deductible_applied_cents + :amt) ... RETURNING deductible_applied_cents does not lose the update: under READ COMMITTED an UPDATE that hits a concurrently updated row blocks, then re-evaluates its expressions against the newly committed version, so both increments land. The catch is that the member's share is the delta this statement actually applied, which is the returned value minus the row's pre-image — and RETURNING does not expose the pre-image before PostgreSQL 18's OLD/NEW aliases. On earlier versions you must read that pre-image under the same row lock, which puts you back in fix one with a shorter critical section. The one-statement form is a clean fix only when the caller does not need the delta.
Follow-up
- A claim is reversed six months later under a plan design that has since changed. What amount does the reversal subtract, and where is that number stored?
- Your retry loop hits 40001 repeatedly for one member during a batch window. What is the backoff, and at what point do you stop retrying and pend the claim?
- Which of the three fixes survives a retroactive eligibility change that invalidates everything applied in the last month, and what does the rebuild look like?
How do you approach building a working application from scratch?
How do you approach building a working application from scratch?
Approach
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer contains.
- Work from the requirement backwards to the design.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
What are you most comfortable with: front-end, back-end, or the full s…
What are you most comfortable with: front-end, back-end, or the full stack?
Approach
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
- Clarify what is being asked and what a complete answer contains.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
What specific technologies or frameworks are you most proficient in?
What specific technologies or frameworks are you most proficient in?
Approach
- Say what you would check first and why it is the highest-information step.
- State your assumptions explicitly before working the problem.
- Work from the requirement backwards to the design.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Model an order placement so a lost response stays recoverable
An ordering system POSTs a medication or lab order to /orders. It assigns a placer identifier; the performing system later assigns a filler identifier, and neither side controls both namespaces. The caller times out after 3s and cannot distinguish an order that never arrived, one that was accepted with the acknowledgement lost, and one still in flight. Duplicating an order is a patient-safety event and silently abandoning one is worse. Design the contract so the caller can always determine the true outcome without guessing, and specify its behaviour at the timeout, on retry, and after repeated failure.
Approach
- Remove the ambiguity at its source by letting the caller name the resource before it exists, so the identity of the attempt never depends on our response arriving. The placer identifier is already caller-assigned and unique within the caller's namespace, so the server key is (placer_namespace, placer_order_id) under a unique constraint. A timeout stops being an unknown and becomes a question with a stable key.
- Prefer the shape that makes the retry trivially safe. PUT /orders/{placer_namespace}/{placer_order_id} is idempotent by HTTP semantics and needs no key header; POST /orders needs an Idempotency-Key to reach the same place. Under either, the write is one INSERT ... ON CONFLICT DO NOTHING, because a check-then-insert lets two retries through under READ COMMITTED. A repeat with an identical canonical body returns the existing resource; a repeat with a different body is 409 rather than a silent replace, since an order is not a document to overwrite.
- Give the caller a read that resolves a timeout without writing: GET on the same key returns accepted, routed, filled with its filler_order_id, or rejected with a reason, and 404 means we genuinely never saw it. Without that read, the caller's only instrument is another write, which is precisely the behaviour being designed out.
- Be honest about what acceptance means. Accept is durably persisted and queued for the performing system, not performed, so return 202 with the order resource and a status the caller polls or subscribes to. Returning 201 for an order that is not yet routed makes the caller believe a stronger fact than is true, and the filler identifier arrives later on the performing system's own timeline.
- Write the caller's behaviour explicitly, because the contract is only half the design: at timeout, GET the key; on 404, retry the write with the same key; on 5xx, exponential backoff with full jitter up to a bounded attempt count; after five failures, stop and raise a human-visible alert carrying the placer identifier. A queued-for-human state is a better outcome than either a duplicate order or a silent drop, and this domain cannot absorb the drop.
Worked solution 40 min
- Draw the three timelines a 3s timeout can hide - request lost, work done and response lost, work still in flight - and mark what the caller can distinguish in each, with and without a caller-assigned key.
- Write the resource path, the unique constraint, and the single atomic statement that performs the write.
- Write the state machine the GET exposes, state what 404 means, and name the one state that must never be inferred from a timeout.
- Write the caller's pseudocode for timeout, retry, backoff and give-up, with the attempt count and the alert payload.
- Run two concurrent retries of the same placer identifier against a real database and confirm one order exists and both callers observe the same state.
Follow-up
- The performing system returns a filler identifier for an order we have no record of. What do you do with it?
- The caller reissues an old placer identifier for a genuinely different order after a counter rollover. What breaks, and how would you detect it?
- Where does the record of the failed attempts live, and what must it carry for an incident review?
Evening eligibility denials cluster in western time zones
Eligibility denials rose from 0.4% to 3.1% of responses. They cluster after 17:00 local time, only at facilities in UTC-5 through UTC-10, and spike on the first and last day of each month. coverage_span.effective_date and termination_date are date columns in business time; the service derives the service date from encounter.admit_ts, a timestamptz. The enrolment files for the affected members look correct on inspection. Give the ordered diagnosis and the fix.
Approach
- Pivot the denial rate two ways before reading code: by facility UTC offset and by local hour of day. A defect that tracks local hour and offset is a time-conversion bug; one that tracks a source file, payer or plan is a data bug. The month-boundary spike is a consequence rather than a second problem, because terminations cluster on month ends and a one-day skew is most visible there.
- Take one denied request and recompute it by hand: the raw admit_ts, its UTC calendar date, the facility-local calendar date under the facility's IANA zone, and the coverage row's effective_date and termination_date. If the answer flips between the UTC date and the local date, the conversion is the cause and no further hypotheses are needed.
- Find the cast. In PostgreSQL, timestamptz::date is evaluated in the session's TimeZone setting, so the identical SQL returns different dates on different pooled connections and a pool that inherits UTC yields the UTC calendar date. date columns carry no zone at all, so comparing a date column to a UTC-derived date compares two different calendars.
- Fix once, at the edge: resolve the service date as (admit_ts AT TIME ZONE facility.iana_zone)::date and pass it down as an explicit date parameter. Use the IANA zone name, never a stored numeric offset, because the offset changes twice a year under daylight saving and a stored -8 is wrong for roughly eight months.
- Forbid re-derivation below the edge and pin the boundary with a frozen-clock test: an encounter at 23:30 local at a UTC-10 facility on the last day of a month whose coverage terminates that day, plus the same encounter one minute later. A test that calls now() passes for most of the day and hides the defect.
Follow-up
- Where else does the as-of date enter the system — the eligibility cache key, claim_line.service_from_date, the accumulator's plan year? Which of those are already computed in UTC?
- An inpatient encounter spans midnight local. Which date governs eligibility — the admit date or the service line date — and at which layer is that decided?
Roughly ninety minutes on weeknights with one longer weekend block. The plan cuts scope rather than compressing everything, on the assumption that one thing finished per night beats four half-started.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and take a cold baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list and keep it visible all week.
- Take one twenty-five-minute coding problem and one fifteen-minute design prompt cold, and write the single sentence naming what blocked each, because those two sentences decide where the remaining evenings go.
- Set the week's rule: one thing finished every night, including the night you only have forty minutes.
Deliverable: A one-page scope with a not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Worked solution ↗02One pattern, written three times from blank
- Choose the single pattern most likely to appear in your loop and write it three times from an empty file rather than editing the previous attempt.
- On the third pass, write the invariant as a comment before the loop body and the complexity before the first line of code.
- Stop at ninety minutes even if the third version is imperfect, and write the one thing you would fix given another hour.
Deliverable: Three independent implementations of the same pattern plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗03One design, only to the depth you can defend
- Take one system shape and go only as far as requirements, interface and data model, refusing to draw a box you could not survive a follow-up about.
- Attach one number to each non-functional requirement, deriving it rather than asserting it, and write the assumption the number rests on.
- Write the one tradeoff you are choosing against and the observation that would make you reverse it.
Deliverable: One design at interface-and-schema depth with derived numbers and one written reversible tradeoff.
Practice prompt ↗Practice prompt ↗04Only the fundamentals you will have to defend
- Write, in under two hundred words each, the answers to the two questions that follow almost any implementation: why this structure and not the obvious alternative, and what happens to this code at a hundred times the input.
- Write what an index actually costs: faster lookups on the indexed columns against a write that now maintains a second structure, plus the cases where the planner declines to use it anyway, low selectivity, or a predicate wrapping the column in a function.
- Delete any answer you cannot deliver aloud in under a minute, since an answer that needs reading is not an answer you have.
Deliverable: Three written answers, each under two hundred words and each timed aloud.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a ninety-second and a four-minute version of your main project and time both aloud rather than reading them.
- Prepare the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be ready to say exactly where it came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in the weekend block
- Run a sixty-minute mock covering a coding round and a design round in one sitting with no break, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the twenty-minute warm-up you will actually do on the morning: one problem you can already solve from a blank file, one design you can narrate, and nothing you have never seen.
- Re-read only your own notes from this week and open no new material.
- Write the logistics down: the editor or shared document you will be working in, whether execution and lookups are permitted, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the design structure, the project numbers, and the logistics.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Keep one story where the bad call was yours rather than a dependency's or a manager's. Name the check that would have caught it, whether you added that check afterwards, and whether it has fired since. Answers that route blame outward end the conversation early; answers that end in a guardrail someone still relies on tend to open it up.
What do you enjoy doing in your professional work?
What do you enjoy doing in your professional work?
Approach
- Give the blast radius: what could have broken, and what you measured.
- Pick a story where you made the decision, not one where you watched it.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you handle disagreements with managers or team leads?
How do you handle disagreements with managers or team leads?
Approach
- Close with what you would do differently, concretely.
- Pick a story where you made the decision, not one where you watched it.
- Give the blast radius: what could have broken, and what you measured.
Follow-up
- How did you know your change caused the improvement?
- What would you do differently if you ran that again?
Can you describe a time you had to advocate for a technical decision?
Can you describe a time you had to advocate for a technical decision?
Approach
- State the situation in two sentences and spend the rest on the reasoning.
- Name the disagreement and how you resolved it with evidence.
- Give the blast radius: what could have broken, and what you measured.
Follow-up
- How did you know your change caused the improvement?
- What would you do differently if you ran that again?
- 01
What do you enjoy doing in your professional work?
- 02
How do you handle disagreements with managers or team leads?
- 03
Can you describe a time you had to advocate for a technical decision?
Is this an official AssistRx interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at AssistRx. Rounds and questions reflect what candidates have reported, not a process AssistRx has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical assessments?
The assessments are practical in nature, focusing on your ability to deliver a working application. If you have a solid portfolio and can explain your development process clearly, you will be well-positioned.
PracHub interview research ↗What is the typical timeline for the hiring process?
The process can be quite efficient, with some candidates receiving offers within a few days of their final interview. Expect a fast-paced environment that values decisive action.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates are those who can communicate their technical decisions clearly while demonstrating high professional maturity and a genuine interest in the company's mission.
PracHub interview research ↗How much preparation time is recommended?
While it varies by individual, reviewing your past projects and practicing how you explain technical trade-offs is a high-yield use of your time.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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